checkRd: (-1) get_country_cocirculation_data.Rd:43: Lost braces
    43 | \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)} for detailed methods.
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checkRd: (-1) get_country_intensity_data.Rd:20: Lost braces
    20 | \verb{get_country_intensity data()} returns data on the annual intensity of influenza circulation in each calendar year. Following \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}, we define 1 as the average intensity. Seasons with intensities greater than 1 have more flu A circulation than average, and seasons with intensities less than 1 are mild.
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checkRd: (-1) get_imprinting_probabilities.Rd:34: Lost braces
    34 | Imprinting probabilities are calculated following \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}. Briefly, the model first calculates the probability that an individual's first influenza infection occurs 0, 1, 2, ... 12 years after birth using a modified geometric waiting time model. The annual circulation intensities output by \code{\link[=get_country_intensity_data]{get_country_intensity_data()}} scale the probability of primary infection in each calendar year.
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checkRd: (-1) get_imprinting_probabilities.Rd:38: Lost braces; missing escapes or markup?
    38 | To calculate other kinds of imprinting probabilities (e.g. for specific clades, strains, or to include pediatric vaccination), users can specify custom circulation frequencies as a list, \code{annual_frequencies}. This list must contain one named element for each country in the \code{countries} input vector. Each list element must be a data frame or tibble whose first column is named "year" and contains numeric years from 1918:max(\code{observation_years}). Columns 2:N of the data frame must contain circulation frequencies that sum to 1 across each row, and each column must have a unique name indicating the exposure kind. E.g. column names could be {"year", "H1N1", "H2N2", "H3N2", "vaccinated"} to include probabilities of imprinting by vaccine, or {"year", "3C.3A", "not_3C.3A"} to calculate clade-specific probabilities.  Do not include a naive column. Any number of imprinting types is allowed, but the code is not optimized to run efficiently when the number of categories is very large. Frequencies within the column must be supplied by the user. See \href{https://www.nature.com/articles/s41467-021-24566-y}{Vieira et al. 2021} for methods to estimate circulation frequencies from sequence databases like \href{https://gisaid.org/}{GISAID} or the \href{https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go=database}{NCBI Sequence Database}.
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checkRd: (-1) get_imprinting_probabilities.Rd:38: Lost braces; missing escapes or markup?
    38 | To calculate other kinds of imprinting probabilities (e.g. for specific clades, strains, or to include pediatric vaccination), users can specify custom circulation frequencies as a list, \code{annual_frequencies}. This list must contain one named element for each country in the \code{countries} input vector. Each list element must be a data frame or tibble whose first column is named "year" and contains numeric years from 1918:max(\code{observation_years}). Columns 2:N of the data frame must contain circulation frequencies that sum to 1 across each row, and each column must have a unique name indicating the exposure kind. E.g. column names could be {"year", "H1N1", "H2N2", "H3N2", "vaccinated"} to include probabilities of imprinting by vaccine, or {"year", "3C.3A", "not_3C.3A"} to calculate clade-specific probabilities.  Do not include a naive column. Any number of imprinting types is allowed, but the code is not optimized to run efficiently when the number of categories is very large. Frequencies within the column must be supplied by the user. See \href{https://www.nature.com/articles/s41467-021-24566-y}{Vieira et al. 2021} for methods to estimate circulation frequencies from sequence databases like \href{https://gisaid.org/}{GISAID} or the \href{https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go=database}{NCBI Sequence Database}.
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checkRd: (-1) get_p_infection_year.Rd:24: Lost braces
    24 | \item{baseline_annual_p_infection}{average annual probability of primary infection. The default, 0.28, was estimated using age-seroprevalence data in \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}.}
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checkRd: (-1) get_p_infection_year.Rd:41: Lost braces
    41 | This function modifies the geometric model above to account for changes in annual circulation intensity, so that annual probabilities of primary infection \eqn{p_i} are scaled by the intensity in calendar year i. Details are given in \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}.
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checkRd: (-1) get_template_data.Rd:29: Lost braces
    29 | \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)} for detailed methods.
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